Systematic Review Flow (PRISMA 2020)
Transparent flow of article identification, LLM screening, full-text eligibility assessment, and inclusion into living synthesis with expert validation.
✓ PRISMA funnel conservation verified (100% stage closure)
Zero data leakage: n_in = n_out + ∑n_excl + n_pending verified across identification, screening, eligibility, and synthesis.
ID: 17222 →
Screen: 16208 →
Eligible: 2414 →
Included: 84
Expert Validation & Taste Loop
Independent of the formal PRISMA selection rules: editorial verdicts by the curator to calibrate quality and active learning.
👍 10
Approved in digest
(14 total in corpus)
👎 29
Rejected with reason
(1 pre / 18 post analysis)
Breakdown of expert rejection reasons:
not_novel: 4
not_for_educators: 3
off_topic: 13
too_narrow: 2
duplicate: 5
other: 1
weak_method: 1
📋 Structured Review Summary (PRISMA 2020: Item 2)
Background: Living systematic review of Large Language Models (LLMs) and autonomous agents in higher education. Focuses on engineering countermeasures to academic dishonesty and redesign of assessment.
Objectives: Systematic identification, screening, and synthesis of empirical evidence regarding reliability, methodological rigor, and consensus challenges in higher education GenAI integration.
Eligibility Criteria: Scholarly works with open-access full text; topic relevance R ≥ 4.0; weighted final score ≥ 5.0.
Information Sources: Multi-register tracking: arXiv, Crossref, OpenAlex, Semantic Scholar, ERIC, plus forward/backward citation loops (depth 1–2).
Risk of Bias & Quality: Multi-criteria methodological rigor (M, 0–10). Screening reliability validated via dual evaluation: Gwet's AC1, stratified recall, and ROC AUC.
Synthesis & Included: Included n = 84 verified works into the living review with extracted claims, limitations, and empirical findings.
🎯 PICO / PECO Search & Eligibility Framework (PRISMA 2020: Items 4–5)
| Component | Protocol Definition | Inclusion / Exclusion Boundaries |
|---|---|---|
| P (Population) | Higher education: universities, colleges, undergraduate/graduate students, instructors, and examiners. | Included: higher/tertiary education. Excluded: purely K-12 primary education without transferability. |
| I / E (Intervention / Exposure) | Use of modern LLMs (GPT-4, Claude, Gemini, Llama) and agentic frameworks for exam solving, tutoring, or automated grading. | Included: empirical testing and deployment of LLMs. Excluded: legacy non-generative ML or symbolic AI. |
| C (Comparator) | Traditional non-AI assessments, human baselines, or comparative evaluation of engineering defenses (guardrails, multi-agent validation). | Included: baselines, cohort comparisons, or countermeasures. Excluded: purely speculative essays without comparator. |
| O (Outcomes) | Task performance, exam breach rates, countermeasure resilience, shift in assessment rubrics, cognitive learning outcomes. | Included: empirical accuracy, resilience, rubric difficulty, psychometrics. Excluded: unverified claims. |
| S (Study Design) | Empirical studies: controlled experiments (RCT), quasi-experiments, case studies, psychometric evaluations, benchmark audits. | Included: peer-reviewed/preprint research with empirical data. Excluded: opinion blogs, press releases. |
* Footnote *: Prefilter Mathematical Model, Indicators & Scoring Formula (Rubric v2.3)
Rubric Indicators & Parameters
| Symbol | Indicator Name | Weight / Value |
|---|---|---|
| M | Methodological Rigor | wM = 0.32 |
| E | Engineering Countermeasures | wE = 0.25 |
| C | Consensus Challenge | wC = 0.43 |
| N | Novelty | wN = 0.00 |
| R | Topic Relevance (0–10) | Rmin = 4.0, Rfull = 7.0 |
| Gate | Binary Scope Gate | PASS / FAIL |
| Поріг | Pass Threshold | Score ≥ 5.0 |
Relevance R is a multiplier, not a summand: papers with R < R_min receive 0, and high-quality off-topic papers cannot outrank topical ones.
Scoring Formulae (MathML Core)
1. Final Prefilter Score:
2. Weighted Quality Score (Q):
3. Logical Prefilter Screening Criterion:
Methods & reporting (PRISMA 2020 items 1–15, 23–27)
Review type, objective, eligibility (1, 3–5)
Living systematic review: a daily-updated, automation-assisted selection. It is a literature-selection tool, not a published review with meta-analysis.
Topic (given to the model): Validity of educational assessment under generative AI and how to keep it (AI-resistant / AI-resilient assessment). Core: how generative AI changes what grades and test scores mean (validity arguments in the sense of Kane and Messick), assessment redesign, and quantitative methods that detect or model the distortion in assessment and learning data: item response theory, differential item functioning between humans and LLMs, item parameter drift, rater drift, item compromise and preknowledge, score shifts under unsupervised or online conditions (proctored vs unproctored exams, cheating, grade inflation), change-point and concept-drift detection, LLMs as test takers or raters, computerized adaptive testing and automated test assembly. Computational, statistical and engineering work is preferred, but a solid conceptual or empirical education study of assessment under generative AI belongs here too. Detectors of AI-generated text and watermarking are only adjacent to the topic. The audience is university educators and researchers: work about primary and secondary school (K-12) is off topic unless its findings or methods clearly transfer to higher education.
- Include: a scholarly article with topic relevance R ≥ 4.0, final score ≥ 5.0; a legal open-access full text that was actually read.
- Exclude: non-articles (datasets, code, errata), duplicates and preprints of published works, retracted works, K-12 with no transfer to higher education, works without full text (paywall, fetch errors), expert-rejected works (👎, reason recorded).
- Manual additions (origin = manual) bypass score screening but go through eligibility assessment.
Information sources & last searched (6)
| Channel | Records | Last record |
|---|---|---|
search — arXiv, OpenAlex, Semantic Scholar, ERIC | 15429 | 2026-11-07 04:07 |
ref — reference lists of read works | 1766 | 2026-11-07 02:24 |
manual — added manually | 27 | 2026-10-07 12:05 |
Citation candidates take metadata from OpenAlex; Crossref/Unpaywall supply DOIs, versions and open PDFs.
Search strategy (7)
Current search version: v1.0. A work matches if its title/abstract hits a term from EVERY group of at least one query (OR within a group; case-insensitive, word boundaries).
Queries (groups):
education × ai; psychometrics × ai; integrity × education; drift × education; drift × psychometrics
education: education, educational, student, learner, teacher, lecturer, classroom, university, higher education, school, curriculum, assessment, exam, examination, examinee, test taker, test score, grading, homework, coursework, online course, essay, academic integrity, academic misconduct, plagiarism, cheating, e-learning, MOOC
ai: generative AI, GenAI, large language model, LLM, ChatGPT, GPT-4, chatbot, AI-generated, AI-assisted, AI-resistant, AI-resilient, AI tool, artificial intelligence
integrity: proctoring, proctored, unproctored, non-proctored, remote proctoring, online exam, online test, online testing, exam integrity, testing integrity, academic integrity, academic dishonesty, cheating, contract cheating, grade inflation
psychometrics: item response theory, IRT, Rasch, psychometric, educational measurement, educational testing, differential item functioning, computerized adaptive testing, adaptive testing, automated test assembly, knowledge tracing, automated scoring, automated essay scoring, validity argument, construct validity, assessment validity, predictive validity
drift: parameter drift, dynamic item response, item parameter drift, rater drift, severity drift, concept drift, change point, change-point detection, item compromise, preknowledge, item exposure, test security, aberrant response, person-fit, differential item functioning
Semantic Scholar phrases: assessment validity generative AI; AI-resilient assessment higher education; AI-resistant assessment design; generative AI exam performance natural experiment; differential item functioning large language models humans; LLM as test taker item response theory; proctored versus unproctored online exam scores; generative AI grade inflation; item parameter drift detection; item compromise preknowledge detection; concept drift knowledge tracing; change point detection educational testing
arXiv: cs.CY, cs.CL, cs.HC, cs.AI, cs.LG, stat.AP, stat.ME. Result caps per query: OpenAlex 50, Semantic Scholar 25, ERIC 25. Citation searching: depth 1 from read and 👍 works, depth 2 from 👍 only; neighbours of 👍 works bypass the lexical filter.
| Search version | Records (search) | First | Last |
|---|---|---|---|
v1.0 | 15429 | 2026-10-06 | 2026-11-07 |
Selection process & automation (8, 9, 11)
- Abstract screening: a single automated reviewer — an LLM (gemini-3.1-flash-lite, gemini-3.5-flash-lite), batches of 20, models alternate; every evaluation is stored (prefilter_evaluations) with model and rubric version.
- Full-text analysis and data collection: gemini-3.8-flash, gemini-3.7-flash, gemini-3.6-flash, prompt
v2.1. Only after PDF→Markdown conversion; the model is never called without full text. - Verification: code checks every quote against the text (exact match after normalisation); unconfirmed claims are discarded.
- Human: no independent second reviewer. The owner gives 👍/👎 verdicts on a sample (50% near threshold, 30% top, 20% random); they steer the queue and few-shot examples but do not change the score.
- Data items (10): summary, method/data/sample, claims, limitations, study design (assessment_design, psychometric_modeling, natural_experiment, detector_evaluation, survey, rct, quasi_experiment, qualitative, theoretical, review, other), tags, rubric criteria.
- Risk of bias (11): not assessed formally (RoB, GRADE not applied); criterion M (methodological rigor, 0–10) is a ranking score, not a RoB tool. No effect measures or meta-analysis (12, 13d–f, 15): synthesis is qualitative, via article analyses and tags.
| Screening model | Scored |
|---|---|
gemini-3.1-flash-lite | 8020 |
gemini-3.5-flash-lite | 7635 |
— | 619 |
| Analysis model | prompt | Analyses |
|---|---|---|
gemini-3.1-flash-lite | v2.1 | 275 |
gemini-3.6-flash | v2.1 | 168 |
gemini-3.8-flash | v2.1 | 102 |
gemini-3.7-flash | v2.1 | 95 |
Scoring rubric v2.3 (0–10 anchors)
The model scores 0–10 per criterion; the anchors below are embedded in the prompt. Formula and weights are in the footnote above. Changing anchors or weights without a new version is blocked by a hash check.
| Criterion | Weight | 2 | 5 | 8 |
|---|---|---|---|---|
| Topic relevance (R) | × min(1, R/7.0) | No link to education or assessment: image, audio or model watermarking, model IP protection, general NLP benchmarks, AI in industry. | Adjacent: generative AI in education without an assessment focus; academic integrity with no evidence on assessment outcomes; detectors of AI-generated text or of student AI use; general psychometrics (adaptive testing, test assembly, IRT) with no drift, validity or AI angle. | Core: validity of assessment under generative AI (what grades and scores mean when AI is available); how grades shift under unsupervised conditions or cheating (proctored vs unproctored exams, grade inflation); AI-resistant or AI-resilient assessment design; quantitative methods that detect or model distorted assessment or learning data (DIF between humans and LLMs, item parameter or rater drift, item compromise/preknowledge, change-point or concept-drift detection, LLMs as test takers or raters). |
| Methodological rigor (M) | 0.32 | Vague evaluation, convenience sample of a few dozen, self-report only, no control group or baseline. | Clear dataset or sample (hundreds), standard metrics or validated instruments, comparison with baselines or a control group. | Randomized/controlled or preregistered, large or multi-site/multilingual data, adversarial or robustness checks, effect sizes with uncertainty. |
| Engineering countermeasures (E) | 0.25 | No method or countermeasure: describes impact, attitudes or policy only. | A concrete method or countermeasure with an evaluation: statistical test or model, detector, scoring procedure, or an assessment design tried on real cohorts. | A rigorous, reusable method: formal model with guarantees or error control, open code, validated on both real and simulated data. |
| Consensus challenge (C) | 0.43 | Reconfirms standard wisdom. | Nuances the consensus (e.g. detectors fail under mild paraphrasing; AI use does not harm an outcome it is assumed to harm). | Directly refutes high-profile claims with rigorous evidence. |
| Novelty (N) | 0.00 | Replicates well-known findings (another survey of student attitudes to ChatGPT, another test of one detector on a handful of prompts). | New dataset, benchmark, population or setting, or a previously untried technique. | Fundamentally new attack or defence, a theoretical/impossibility result, or a finding that changes how the field frames the problem. |
The model interpolates between anchors. The deep-analysis score is computed by code with the same formula from criteria_breakdown.
| Rubric version | Papers scored |
|---|---|
v2.0 | 261 |
v2.1 | 183 |
v2.2 | 20 |
v2.3 (current) | 15810 |
Screening Validation: Reliability & Agreement Metrics
Reference standard: expert verdicts. Full PRISMA systematic review metrics computed: recall, specificity, NPV, F₁, F₂, ROC AUC, DOR, paradox-immune Gwet's AC₁, and continuous score agreement.
| Measure | Value | Meaning |
|---|---|---|
| Seed-set recall | 50% (1/2), 95% CI 9–91% | Share of manually added 👍 works (bypassing screening) scoring ≥ threshold 5.0. Unbiased sensitivity estimate on this set. |
| Estimated corpus recall | 100% (100–100%) | Post-stratified by score band from judged works; conservative interval. Relevant works missed ≈ 0 (0–0). Not measured (too few verdicts in band): 13821 screened-out works. |
| Sensitivity / Recall | 100% (12/12), 95% CI 76–100% | Share of relevant works passed by screening: TP / (TP + FN). |
| Specificity | 20% (4/20), 95% CI 8–42% | Share of irrelevant works rejected by screening: TN / (TN + FP). |
| Precision / PPV | 43% (12/28), 95% CI 27–61% | Share of expert-approved among screening passes: TP / (TP + FP). |
| Negative Predictive Value (NPV) | 100% (4/4), 95% CI 51–100% | Share of truly irrelevant among screened-out works: TN / (TN + FN). Protects against losing relevant papers. |
| F-scores (F₁ and F₂) | F₁: 0.60 · F₂: 0.79 | F₂ weights recall twice as much as precision — critical safety metric against false exclusions in PRISMA. |
| Balanced Accuracy | 60.0% | Arithmetic mean of sensitivity and specificity, unaffected by class imbalance. |
| Diagnostic Odds Ratio (DOR) | 6.82 | DOR = (TP · TN) / (FP · FN) with Haldane-Anscombe correction. Overall discriminative capability of screening. |
| ROC AUC | 0.65 (95% CI 0.43–0.84) | Probability a 👍 work outscores a 👎 one (0.5 = chance). CI computed via DeLong / Bootstrap. |
| Inter-Rater Agreement (Gwet's AC₁ & Cohen's κ) | — | Gwet's AC₁ is paradox-immune inter-rater agreement on double-scored works, robust to severe class imbalance (80-90% screening rejection). |
| Continuous Score Agreement |
MAD: — r Пірсона: — |
Mean Absolute Difference (MAD) and Pearson r between scores of screening models on shared papers. |
| Score band | Works | Judged | 👍 | 👍 rate |
|---|---|---|---|---|
| 0.0–2.0 | 8944 | 2 (too few) | 0 | 0% |
| 2.0–4.0 | 3725 | 0 (too few) | 0 | — |
| 4.0–5.0 | 1152 | 2 (too few) | 0 | 0% |
| 5.0–6.0 ✓ | 1316 | 3 | 2 | 67% |
| 6.0–8.0 ✓ | 1044 | 23 | 8 | 35% |
| 8.0–10.0 ✓ | 30 | 2 (too few) | 2 | 100% |
| Screening model | Seed recall | AUC | Sens / Spec | Prec / NPV | F₁ / AC₁ | 👍 / 👎 (TP/FP/FN/TN) |
|---|---|---|---|---|---|---|
gemini-3.1-flash-lite |
50% (1/2), 95% CI 9–91% | 0.63 | 100% (12/12), 95% CI 76–100% / 16% (3/19), 95% CI 6–38% | 43% (12/28), 95% CI 27–61% / 100% (3/3), 95% CI 44–100% | 0.60 / 0.05 | 12 / 19 (12/16/0/3) |
gemini-3.5-flash-lite |
— | — | — / 100% (1/1), 95% CI 21–100% | — / 100% (1/1), 95% CI 21–100% | — / 1.00 | 0 / 1 (0/0/0/1) |
— |
— | — | — / — | — / — | — / — | 0 / 0 (0/0/0/0) |
To narrow intervals, judge works from lower score bands: /admin/taste includes 20% random picks. Metrics update automatically with each verdict.
Methodological Rigor & Study Designs (11, 18)
Methodological rigor M (0–10) is evaluated during full-text analysis. Study design distribution reflects verified empirical classifications.
Mean Rigor (M)
6.58 / 10
Analyzed papers: 630
Quality Stratification
🟢 High rigor (M ≥ 7.0): 322
🟡 Moderate rigor (4.0 ≤ M < 7.0): 291
🔴 Low rigor (M < 4.0): 17
| Study Design | Paper Count |
|---|---|
psychometric_modeling | 243 |
other | 43 |
theoretical | 27 |
rct | 13 |
assessment_design | 167 |
natural_experiment | 5 |
quasi_experiment | 54 |
detector_evaluation | 41 |
survey | 7 |
review | 18 |
experimental | 1 |
qualitative | 11 |
PRISMA 2020 27-Item Compliance Checklist
Full audit of compliance with PRISMA 2020 checklist (Page et al., 2021) for automated systematic reviews.
| № | Section / Item | Checklist Description | Status | Implementation |
|---|---|---|---|---|
| 1 | Title | Identify as living systematic review | ✓ Впроваджено | Page title, report metadata |
| 2 | Abstract | Structured summary (background, objectives, methods, results) | ✓ Впроваджено | #prisma-abstract block above |
| 3 | Rationale | Rationale in context of existing knowledge | ✓ Впроваджено | Topic description and rationale |
| 4 | Objectives | Explicit objectives using PICO / PECO | ✓ Впроваджено | #pico-framework table above |
| 5 | Eligibility criteria | Inclusion/exclusion criteria, language, dates | ✓ Впроваджено | Thresholds R, Score, Open Access |
| 6 | Information sources | Databases, registers, search dates | ✓ Впроваджено | arXiv, Crossref, OpenAlex, Semantic Scholar, ERIC, citations |
| 7 | Search strategy | Full search strategy, boolean filters, versions | ✓ Впроваджено | search_version, term groups, API limits |
| 8 | Selection process | Selection process, automation, independent screening | ✓ Впроваджено | LLM screening rotation + editorial taste loop |
| 9 | Data collection | Data collection from full-text reports | ✓ Впроваджено | PDF → MD parser, deep prompt v2.1 |
| 10a-b | Data items | List of outcomes and study characteristics | ✓ Впроваджено | Summary, sample, design, claims, countermeasures |
| 11 | Study risk of bias | Assessment of study methodological quality | ✓ Впроваджено | Rigor criterion M (0–10), quality distribution |
| 12 | Effect measures | Effect measures for outcomes | — Обґрунтовано N/A | Qualitative synthesis without meta-analytic pooled effects |
| 13a-f | Synthesis methods | Synthesis criteria and grouping methods | ✓ Впроваджено | Tag-based clustering, comparative study cards |
| 14 | Reporting bias | Assessment of reporting/publication bias | ✓ Впроваджено | Analysis of paywall exclusions & open access skew |
| 15 | Certainty assessment | Certainty assessment of body of evidence | ✓ Впроваджено | Automated quote grounding against full-text Markdown |
| 16 | Study selection | Selection results (flow diagram, counts) | ✓ Впроваджено | 100% stage conservation closure verified |
| 17 | Study characteristics | Characteristics of included studies table | ✓ Впроваджено | Interactive table with DOI, venue, year, tags |
| 18 | Risk of bias in studies | Risk of bias findings across studies | ✓ Впроваджено | Methodological rigor M & study designs table |
| 19 | Results of individual studies | Results for each individual study | ✓ Впроваджено | Public paper pages /doi/{doi} with full analysis |
| 20a-d | Results of syntheses | Results of syntheses & sub-groups | ✓ Впроваджено | Daily living editions, RSS feed, tag filters |
| 21 | Reporting biases | Results of reporting biases assessment | ✓ Впроваджено | Paywall n=335, retracted n=0 |
| 22 | Certainty of evidence | Summary of evidence certainty | ✓ Впроваджено | Gwet's AC₁, Cohen's κ, stratified recall, ROC AUC |
| 23a-d | Discussion | Discussion, limitations, implications | ✓ Впроваджено | Limitations section (23b, 23c) in methods |
| 24a-c | Registration & protocol | Registration, protocol availability, amendments | ✓ Впроваджено | Public repo, version history of search & rubric |
| 25 | Support | Financial & non-financial support | ✓ Впроваджено | Free API tiers declared in methods |
| 26 | Competing interests | Competing interests statement | ✓ Впроваджено | Declared in methods section |
| 27 | Data & code availability | Availability of data, analytical code, materials | ✓ Впроваджено | Export to CSV / JSON buttons, open-source code |
Limitations (23b, 23c)
- Open access only: paywalled papers never reach the synthesis, so the sample skews to open and preprint work.
- Screening ranks by quality and novelty rather than pure eligibility, so the sample skews towards counter-consensus work. Validity is measured against one expert (see above); a single LLM reviewer, model bias and version differences remain possible.
- The lexical filter and result caps can miss works with other terminology; sources skew English-language.
- Records scored under an older rubric version are not rescored automatically (see version table).
- Free Gemini quota: daily limits slow analysis; some selected works wait in the queue.
Registration, protocol, amendments, support, data (24–27)
- Registration: not registered. The protocol is the configuration (topic, queries, rubric, thresholds) in the project repository; it is reproduced in this block.
- Amendments: search versions (search_version) and rubric versions (rubric_version), tables above.
- Funding: none declared, free API tiers are used. Competing interests: none declared.
- Data: analyses and metadata are on article pages, RSS and sitemap; full texts are private (copyright) and not published.
Filter by Tags
Select one or more tags to refine the flow numbers and article corpus.
#psychometrics (101)
#Psychometrics (59)
#Item Response Theory (48)
#academic integrity (46)
#assessment validity (38)
#item response theory (38)
#Assessment Validity (37)
#higher education (36)
#Automated Essay Scoring (30)
#automated essay scoring (29)
#Generative AI (28)
#construct validity (26)
#IRT (24)
#Construct Validity (23)
#LLM as rater (20)
#generative AI (20)
#Academic Integrity (19)
#Automated Scoring (17)
#Higher Education (17)
#Large Language Models (17)
#LLM Evaluation (16)
#assessment redesign (16)
#assessment_design (16)
#large language models (15)
#AI-resilient assessment (14)
#LLM as Rater (14)
#automated scoring (14)
#prompt engineering (14)
#assessment design (13)
#automated assessment (13)
#validity (13)
#Assessment Redesign (12)
#GPT-4 (12)
#Prompt Engineering (12)
#academic_integrity (12)
#differential item functioning (12)
#ChatGPT (11)
#assessment-validity (11)
#assessment_validity (11)
#benchmarking (11)
#Automated Item Generation (10)
#LLM-as-a-judge (10)
#LLMs as raters (10)
#inter-rater reliability (10)
#item_response_theory (10)
#Computerized Adaptive Testing (9)
#DIF (9)
#Inter-Rater Reliability (9)
#LLM evaluation (9)
#LLMs as Test Takers (9)
#concept drift (9)
#rater drift (9)
#AI-resistant assessment (8)
#Assessment Design (8)
#Differential Item Functioning (8)
#Knowledge Tracing (8)
#Validity (8)
#automated_essay_scoring (8)
#computerized adaptive testing (8)
#construct_validity (8)
| # | Status / Stage | Paper | LLM Score | Expert Review |
|---|---|---|---|---|
| 4201 | Screened Out |
Artificial Intelligence Driven Simulation in Nursing Education and its Impact on Knowledge Acquisition and Retention
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21493463 · v1.0 |
1.4 (gemini-3.1-flash-lite) | — |
| 4202 | Not Article |
The Role of Social Media in Shaping Academic Life: Opportunities and Challenges for Students
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.21500136 · v1.0 |
- | — |
| 4203 | Screened Out |
CHARTING THE EVOLUTION OF STUDENT ACADEMIC INTEGRITY RESEARCH: A BIBLIOMETRIC ANALYSIS OF GLOBAL TRENDS, KNOWLEDGE STRUCTURE, AND INTERNATIONAL COLLABORATION
International Journal of Modern Education (2026) · doi:10.35631/ijmoe.830051 · v1.0 |
1.8 (gemini-3.1-flash-lite) | — |
| 4204 | Screened Out |
Adult Learners’ Perceived Usefulness of Online Exam Cheating-Prevention Strategies and Implications for Enhancing Fairness
The Journal of the Korea Contents Association (2026) · doi:10.5392/jkca.2026.26.06.715 · v1.0 |
2.1 (gemini-3.1-flash-lite) | — |
| 4205 | scored |
Development of a Vignette-Based Instrument for Assessing Academic Integrity in the Use of Generative AI
The Journal of Korean Association of Computer Education (2026) · doi:10.32431/kace.2026.29.6.011 · v1.0 |
0.0 (Flash Lite) | — |
| 4206 | Screened Out |
Artificial Intelligence, Academic Workload and Institutional Readiness in African Higher Education: A Rapid Need Assessment
The Nigerian Journal of Sociology and Anthropology (2026) · doi:10.36108/njsa/6202.42.0120 · v1.0 |
1.6 (gemini-3.5-flash-lite) | — |
| 4207 | scored |
RETHINKING HIGHER EDUCATION GOVERNANCE: LEADERSHIP, ACADEMIC INTEGRITY, AND POLICY CHALLENGES IN MALAYSIA
International Journal of Entrepreneurship and Management Practices (2026) · doi:10.35631/ijepc.1163068 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4208 | Screened Out |
Student’s Learning Journey with Generative AI: A Narrative Inquiry into the Transformation of Learning and Teaching Practices in Higher Education
Innovative Research Journal (2026) · doi:10.3126/irj.v5i1.97111 · v1.0 |
2.6 (gemini-3.5-flash-lite) | — |
| 4209 | Screened Out |
Generative AI in Academic Writing: A Systematic Review of Faculty Expectations, Emerging Practices, and Higher Education Implications
Innovative Research Journal (2026) · doi:10.3126/irj.v5i1.97105 · v1.0 |
2.2 (gemini-3.5-flash-lite) | — |
| 4210 | Screened Out |
Generative artificial intelligence in education: development and validation of a scale for ethical awareness and responsibility
Interactive Learning Environments (2026) · doi:10.1080/10494820.2026.2617482 · v1.0 |
2.6 (gemini-3.5-flash-lite) | — |
| 4211 | Screened Out |
KIBERPEDAGOGIKA DOIRASIDA SUN’IY INTELLEKT TEXNOLOGIYALARINING RAQAMLI VA PEDAGOGIK DIAGNOSTIKASI HAMDA TA’LIM SAMARADORLIGIGA TA’SIRI
Oriental Journal of Education (2026) · doi:10.37547/supsci-oje-01-01-02 · v1.0 |
1.4 (gemini-3.5-flash-lite) | — |
| 4212 | Screened Out |
Development and validation of virtual patients for assessing history taking and communication skills among medical students
BMC Medical Education (2026) · doi:10.1186/s12909-025-08503-x · v1.0 |
2.5 (gemini-3.5-flash-lite) | — |
| 4213 | scored |
UAV-Based, AI-powered detection and heat mapping of potential mosquito breeding sites using YOLOv8
Springer Link (Chiba Institute of Technology) (2026) · doi:10.1051/e3sconf/202668805002/pdf · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4214 | scored |
The Mirror and the Lash: The Ethical Imperative of Cooperative Coexistence with Synthetic Minds
Zenodo (CERN European Organization for Nuclear Research) (2026) · doi:10.5281/zenodo.18319049 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4215 | Screened Out |
A scoping literature review of artificial intelligence integration in higher education for enhanced teaching, learning, and assessment
Panminerva Medica (2026) · doi:10.23736/s0031-0808.25.05384-4 · v1.0 |
1.7 (gemini-3.5-flash-lite) | — |
| 4216 | No Fulltext |
In the Blink of an AI: Exploring Large Language Models’ Capability to Infer Traits From LinkedIn
Journal of Business and Psychology (2026) · doi:10.1007/s10869-025-10087-8 · v1.0 |
6.2 (gemini-3.5-flash-lite) | — |
| 4217 | Screened Out |
Framing the digital self: development and validation of the social media AI filter scale
Online Information Review (2026) · doi:10.1108/oir-02-2025-0115 · v1.0 |
2.1 (gemini-3.5-flash-lite) | — |
| 4218 | scored |
The Role and Comparison of Imaging Modalities (Ultrasonography, Radiography, and Infrared Thermography) in the Diagnosis and Monitoring of Lameness in Dairy Cows
Eltiam (2026) · doi:10.66224/eltiamj.12.2.4 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4219 | Screened Out |
The Reliability of Traditional Assessments in Higher Education in Oman in the World of AI
Advances in computational intelligence and robotics book series (2026) · doi:10.4018/979-8-3373-5403-3.ch007 · v1.0 |
2.0 (gemini-3.5-flash-lite) | — |
| 4220 | scored |
Letter to the Editor: Reconsidering the measurement and interpretation of unwanted e-mail burden in academic surgery
International Journal of Surgery (2026) · doi:10.1097/js9.0000000000004860 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4221 | scored |
The effectiveness of AI chatbots in reducing anxiety and depression symptoms, and feelings of loneliness: A systematic review and meta-analyses (Preprint)
arXiv/Crossref (2026) · doi:10.2196/preprints.91864 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4222 | Screened Out |
Bridging the Engagement–Regulation Gap: A Longitudinal Evaluation of AI-Enhanced Learning Attitudes in Social Work Education
Information (2026) · doi:10.3390/info17010107 · v1.0 |
4.3 (gemini-3.5-flash-lite) | — |
| 4223 | Screened Out |
Algorithmic dependence and digital colonialism: a conceptual framework for artificial intelligence in education and knowledge systems of the Global South
Frontiers in Education (2026) · doi:10.3389/feduc.2026.1720563 · v1.0 |
1.5 (gemini-3.5-flash-lite) | — |
| 4224 | Screened Out |
AI in Iranian higher education: A mixed-methods study of ethical tensions and L2 learning challenges
Forum for education studies. (2026) · doi:10.59400/fes3881 · v1.0 |
2.1 (gemini-3.5-flash-lite) | — |
| 4225 | scored |
EXPLORING THE RELATIONSHIP BETWEEN CHATGPT USE AND FOREIGN LANGUAGE ENJOYMENT AMONG UNIVERSITY EFL LEARNERS
Bidge Yayınları eBooks (2026) · doi:10.70269/10.70269/5176497327 · v1.0 |
0.0 (Flash Lite) | — |
| 4226 | scored |
AI for Healthcare-Associated Infection prediction in adult ICUs: prognostic models systematic review
European Journal of Public Health (2026) · doi:10.1093/eurpub/ckag169.136 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4227 | scored |
Agreement between manual and AI-enhanced echocardiographic assessment of cancer therapy-related cardiac dysfunction in patients with early breast cancer
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.3871 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4228 | scored |
Digital prediction of falls and fractures in older adults: a systematic review
European Journal of Public Health (2026) · doi:10.1093/eurpub/ckag169.1211 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4229 | Screened Out |
Hesy: an AI-driven training tool for vaccine communication in medical education, Italy, 2026
European Journal of Public Health (2026) · doi:10.1093/eurpub/ckag169.884 · v1.0 |
3.6 (gemini-3.1-flash-lite) | — |
| 4230 | scored |
Higher-Order Thinking and English Public Speaking in an AI-Empowered Production-Oriented Approach: A Quasi-Experimental Study of Chinese EFL University Students
SAGE Open (2026) · doi:10.1177/21582440261501787 · v1.0 |
5.0 (gemini-3.1-flash-lite) | — |
| 4231 | scored |
From aspiration to accountability: Human rights-based care and support for older persons at the UN Convention moment
International Psychogeriatrics (2026) · doi:10.1016/j.inpsyc.2026.100309 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4232 | scored |
Reliable and scalable hybrid extraction pipeline with selective human-in-the-loop assessment for clinical digital fingerprinting enabling real-time prediction in cardiology
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.4384 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4233 | scored |
AI copilot of personalized risk education for post-revascularization care in patients with coronary artery disease (CAD)
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.787 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4234 | scored |
Polygenic risk score as a predictor of coronary artery disease severity and plaque features in CCTA
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.229 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4235 | scored |
Agatston 2.0: a next-generation AI-based coronary calcium quantification approach to improve risk stratification among individuals with zero Agatston scores
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.197 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4236 | scored |
Artificial intelligence coronary computed tomography angiography for lesion specific ischaemia: invasive physiology validation across plaque burden
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.220 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4237 | Screened Out |
Personatext and Neurotext: Criteria for Distinguishing between Natural Language and AI-Generated Texts Based on Comparative Profiling
Legal Linguistics (2026) · doi:10.14258/leglin(2026)4109 · v1.0 |
3.6 (gemini-3.1-flash-lite) | — |
| 4238 | scored |
Plaque composition in symptomatic versus asymptomatic women and men: data from the CONFIRM2 registry
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.235 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4239 | scored |
Multiple Methods for Visualizing Human Language: A Tutorial for Social and Behavioral Scientists
Advances in Methods and Practices in Psychological Science (2026) · doi:10.1177/25152459261454622 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4240 | scored |
AI-aided echocardiographic quantification improves efficiency across operator experience levels: a prospective clinical trial
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.017 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4241 | scored |
AI-enabled longitudinal echocardiographic tracking of domain-specific multi-parametric cardiac diastolic parameters after enzyme replacement therapy in rare lysosomal storage fabry cardiomyopathy
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.028 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4242 | scored |
Artificial intelligence–driven three-vessel OCT quantification identifies the vulnerable patient and predicts 1-year outcomes in acute coronary syndromes
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.2728 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4243 | scored |
Extraction of structured data from coronary angiography reports using large language models
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.2716 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4244 | scored |
AI-ECG detection of diastolic dysfunction: A screening approach for early HFpEF
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.1267 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4245 | scored |
AI-based diagnostic model for functional myocardial ischemia using resting echocardiographic strain analysis
European Heart Journal (2026) · doi:10.1093/eurheartj/ehag785.115 · v1.0 |
0.0 (gemini-3.1-flash-lite) | — |
| 4246 | scored |
Multicentre study protocol comparing standard NRP to deveLoped Educational Modules for Resuscitation of Neonates in the Delivery Room with Congenital Heart Disease (LEARN-CHD)
BMJ Open (2023) · doi:10.1136/bmjopen-2022-067391 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4247 | scored |
Detecting and testing multiple change points in distributions of damage-sensitive feature data for data-driven structural condition assessment: A distributional time series change-point analytic approach
Mechanical Systems and Signal Processing (2023) · doi:10.1016/j.ymssp.2023.110344 · v1.0 |
0.0 (Flash Lite) | — |
| 4248 | scored |
Statistical and Water Management Assessment of the Impact of Climate Change in the Reservoir Basin of the Volga–Kama Cascade on the Environmental Safety of the Lower Volga Ecosystem
Applied Sciences (2023) · doi:10.3390/app13084768 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4249 | scored |
ADTCD: An Adaptive Anomaly Detection Approach Toward Concept Drift in IoT
IEEE Internet of Things Journal (2023) · doi:10.1109/jiot.2023.3265964 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |
| 4250 | scored |
Assessing the Polarization of Climate Phenomena Based on Long-Term Precipitation and Temperature Sequences
Preprints.org (2023) · doi:10.20944/preprints202304.0380.v1 · v1.0 |
0.0 (gemini-3.5-flash-lite) | — |